{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"VirusShare.csv\")\n",
    "df = df.drop('FileName', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SectionAlignment</th>\n",
       "      <td>280.0</td>\n",
       "      <td>4.070400e+03</td>\n",
       "      <td>3.023600e+02</td>\n",
       "      <td>512.0</td>\n",
       "      <td>4096.0</td>\n",
       "      <td>4096.0</td>\n",
       "      <td>4096.0</td>\n",
       "      <td>4.096000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FileAlignment</th>\n",
       "      <td>280.0</td>\n",
       "      <td>8.960000e+02</td>\n",
       "      <td>1.110497e+03</td>\n",
       "      <td>512.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>4.096000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SizeOfHeaders</th>\n",
       "      <td>280.0</td>\n",
       "      <td>1.353143e+03</td>\n",
       "      <td>9.518548e+02</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>4.096000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ImageBase</th>\n",
       "      <td>280.0</td>\n",
       "      <td>1.159578e+08</td>\n",
       "      <td>6.235488e+08</td>\n",
       "      <td>65536.0</td>\n",
       "      <td>4194304.0</td>\n",
       "      <td>4194304.0</td>\n",
       "      <td>4194304.0</td>\n",
       "      <td>6.442451e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SizeOfImage</th>\n",
       "      <td>280.0</td>\n",
       "      <td>2.905655e+05</td>\n",
       "      <td>7.704056e+05</td>\n",
       "      <td>16384.0</td>\n",
       "      <td>28672.0</td>\n",
       "      <td>53248.0</td>\n",
       "      <td>180224.0</td>\n",
       "      <td>4.034560e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DllCharacteristics</th>\n",
       "      <td>280.0</td>\n",
       "      <td>3.097143e+03</td>\n",
       "      <td>9.690773e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.404800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Characteristics</th>\n",
       "      <td>280.0</td>\n",
       "      <td>1.321318e+03</td>\n",
       "      <td>2.745006e+03</td>\n",
       "      <td>34.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>8.462000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HighEntropy</th>\n",
       "      <td>280.0</td>\n",
       "      <td>8.928571e-02</td>\n",
       "      <td>2.856663e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowEntropy</th>\n",
       "      <td>280.0</td>\n",
       "      <td>9.285714e-02</td>\n",
       "      <td>2.907518e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalSuspiciousSections</th>\n",
       "      <td>280.0</td>\n",
       "      <td>3.071429e-01</td>\n",
       "      <td>6.093234e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalNonSuspiciousSections</th>\n",
       "      <td>280.0</td>\n",
       "      <td>3.560714e+00</td>\n",
       "      <td>5.453218e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count          mean           std      min  \\\n",
       "SectionAlignment            280.0  4.070400e+03  3.023600e+02    512.0   \n",
       "FileAlignment               280.0  8.960000e+02  1.110497e+03    512.0   \n",
       "SizeOfHeaders               280.0  1.353143e+03  9.518548e+02   1024.0   \n",
       "ImageBase                   280.0  1.159578e+08  6.235488e+08  65536.0   \n",
       "SizeOfImage                 280.0  2.905655e+05  7.704056e+05  16384.0   \n",
       "DllCharacteristics          280.0  3.097143e+03  9.690773e+03      0.0   \n",
       "Characteristics             280.0  1.321318e+03  2.745006e+03     34.0   \n",
       "HighEntropy                 280.0  8.928571e-02  2.856663e-01      0.0   \n",
       "LowEntropy                  280.0  9.285714e-02  2.907518e-01      0.0   \n",
       "TotalSuspiciousSections     280.0  3.071429e-01  6.093234e-01      0.0   \n",
       "TotalNonSuspiciousSections  280.0  3.560714e+00  5.453218e-01      1.0   \n",
       "\n",
       "                                  25%        50%        75%           max  \n",
       "SectionAlignment               4096.0     4096.0     4096.0  4.096000e+03  \n",
       "FileAlignment                   512.0      512.0      512.0  4.096000e+03  \n",
       "SizeOfHeaders                  1024.0     1024.0     1024.0  4.096000e+03  \n",
       "ImageBase                   4194304.0  4194304.0  4194304.0  6.442451e+09  \n",
       "SizeOfImage                   28672.0    53248.0   180224.0  4.034560e+06  \n",
       "DllCharacteristics                0.0        0.0        0.0  3.404800e+04  \n",
       "Characteristics                 271.0      271.0      271.0  8.462000e+03  \n",
       "HighEntropy                       0.0        0.0        0.0  1.000000e+00  \n",
       "LowEntropy                        0.0        0.0        0.0  1.000000e+00  \n",
       "TotalSuspiciousSections           0.0        0.0        1.0  4.000000e+00  \n",
       "TotalNonSuspiciousSections        3.0        4.0        4.0  4.000000e+00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Quality Report\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Type</th>\n",
       "      <th>Count</th>\n",
       "      <th>Unique Values</th>\n",
       "      <th>Minimum Values</th>\n",
       "      <th>Maximum Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SectionAlignment</th>\n",
       "      <td>int64</td>\n",
       "      <td>280</td>\n",
       "      <td>2</td>\n",
       "      <td>512</td>\n",
       "      <td>4096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FileAlignment</th>\n",
       "      <td>int64</td>\n",
       "      <td>280</td>\n",
       "      <td>2</td>\n",
       "      <td>512</td>\n",
       "      <td>4096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SizeOfHeaders</th>\n",
       "      <td>int64</td>\n",
       "      <td>280</td>\n",
       "      <td>2</td>\n",
       "      <td>1024</td>\n",
       "      <td>4096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TimeDateStamp</th>\n",
       "      <td>object</td>\n",
       "      <td>280</td>\n",
       "      <td>200</td>\n",
       "      <td>0x44D1EFCE [Thu Aug  3 12:45:02 2006 UTC]</td>\n",
       "      <td>0x50D9A526 [Tue Dec 25 13:07:50 2012 UTC]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ImageBase</th>\n",
       "      <td>int64</td>\n",
       "      <td>280</td>\n",
       "      <td>6</td>\n",
       "      <td>65536</td>\n",
       "      <td>6442450944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SizeOfImage</th>\n",
       "      <td>int64</td>\n",
       "      <td>280</td>\n",
       "      <td>63</td>\n",
       "      <td>16384</td>\n",
       "      <td>4034560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DllCharacteristics</th>\n",
       "      <td>int64</td>\n",
       "      <td>280</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>34048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Characteristics</th>\n",
       "      <td>int64</td>\n",
       "      <td>280</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>8462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HighEntropy</th>\n",
       "      <td>int64</td>\n",
       "      <td>280</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowEntropy</th>\n",
       "      <td>int64</td>\n",
       "      <td>280</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalSuspiciousSections</th>\n",
       "      <td>int64</td>\n",
       "      <td>280</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalNonSuspiciousSections</th>\n",
       "      <td>int64</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Data Type  Count Unique Values  \\\n",
       "SectionAlignment               int64    280             2   \n",
       "FileAlignment                  int64    280             2   \n",
       "SizeOfHeaders                  int64    280             2   \n",
       "TimeDateStamp                 object    280           200   \n",
       "ImageBase                      int64    280             6   \n",
       "SizeOfImage                    int64    280            63   \n",
       "DllCharacteristics             int64    280             6   \n",
       "Characteristics                int64    280             9   \n",
       "HighEntropy                    int64    280             2   \n",
       "LowEntropy                     int64    280             2   \n",
       "TotalSuspiciousSections        int64    280             5   \n",
       "TotalNonSuspiciousSections     int64    280             4   \n",
       "\n",
       "                                                       Minimum Values  \\\n",
       "SectionAlignment                                                  512   \n",
       "FileAlignment                                                     512   \n",
       "SizeOfHeaders                                                    1024   \n",
       "TimeDateStamp               0x44D1EFCE [Thu Aug  3 12:45:02 2006 UTC]   \n",
       "ImageBase                                                       65536   \n",
       "SizeOfImage                                                     16384   \n",
       "DllCharacteristics                                                  0   \n",
       "Characteristics                                                    34   \n",
       "HighEntropy                                                         0   \n",
       "LowEntropy                                                          0   \n",
       "TotalSuspiciousSections                                             0   \n",
       "TotalNonSuspiciousSections                                          1   \n",
       "\n",
       "                                                       Maximum Values  \n",
       "SectionAlignment                                                 4096  \n",
       "FileAlignment                                                    4096  \n",
       "SizeOfHeaders                                                    4096  \n",
       "TimeDateStamp               0x50D9A526 [Tue Dec 25 13:07:50 2012 UTC]  \n",
       "ImageBase                                                  6442450944  \n",
       "SizeOfImage                                                   4034560  \n",
       "DllCharacteristics                                              34048  \n",
       "Characteristics                                                  8462  \n",
       "HighEntropy                                                         1  \n",
       "LowEntropy                                                          1  \n",
       "TotalSuspiciousSections                                             4  \n",
       "TotalNonSuspiciousSections                                          4  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DataFrame with columns\n",
    "columns = pd.DataFrame(list(df.columns.values[1:]))\n",
    "\n",
    "#DataFrame with data types\n",
    "data_types = pd.DataFrame(df.dtypes, columns=['Data Type'])\n",
    "\n",
    "#DataFrame with Count\n",
    "data_count = pd.DataFrame(df.count(), columns=['Count'])\n",
    "\n",
    "#DataFrame with unique values\n",
    "unique_value_counts = pd.DataFrame(columns=['Unique Values'])\n",
    "for v in list(df.columns.values):\n",
    "    unique_value_counts.loc[v] = [df[v].nunique()]\n",
    "\n",
    "#DataFrame with minimum values\n",
    "minimum_values = pd.DataFrame(columns=['Minimum Values'])\n",
    "for v in list(df.columns.values):\n",
    "    minimum_values.loc[v] = [df[v].min()]\n",
    "\n",
    "#DataFrame with maximum values\n",
    "maximum_values = pd.DataFrame(columns=['Maximum Values'])\n",
    "for v in list(df.columns.values):\n",
    "    maximum_values.loc[v] = [df[v].max()]\n",
    "        \n",
    "data_quality_report = data_types.join(data_count).join(unique_value_counts).join(minimum_values).join(maximum_values)\n",
    "print('Data Quality Report')\n",
    "data_quality_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In ImageBase feature, 4194304 look like as a standard value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In ImageBase feature, there are 208 instances have 0 suspicious section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In DllCharacteristics feature, 0 look like as a standard value (252 instances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Characteristics feature, 271 look like as a standard value (214 instances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualyzing data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visulyzing *Characteristics* feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = df['Characteristics'].value_counts().keys().tolist()\n",
    "counts = df['Characteristics'].value_counts().tolist()\n",
    "ids = [x for x in range(len(values))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "print(ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 9 artists>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(ids,values,rotation='45')\n",
    "plt.bar(ids,counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualyzing *ImageBase* feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = df['ImageBase'].value_counts().keys().tolist()\n",
    "counts = df['ImageBase'].value_counts().tolist()\n",
    "ids = [x for x in range(len(values))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 6 artists>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(ids,values,rotation='45')\n",
    "plt.bar(ids,counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualyzing three feature *TotalSuspiciousSections, DllCharacteristics, ImageBase*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.index.tolist()\n",
    "y1 = df['TotalSuspiciousSections'].tolist()\n",
    "y2 = (df['DllCharacteristics']/10000).tolist()\n",
    "y3 = (df['ImageBase']/1000000000).tolist()\n",
    "\n",
    "plt.scatter(x,y1,s=2, label=\"Total Suspicious Sections\", color=\"r\")\n",
    "plt.scatter(x,y2,s=2, label=\"Total Suspicious Sections\", color=\"g\")\n",
    "plt.scatter(x,y3,s=2, label=\"Total Suspicious Sections\", color=\"b\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualyzing two feature *TotalSuspiciousSections, DllCharacteristics* in 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.index.tolist()\n",
    "y = df['TotalSuspiciousSections'].tolist()\n",
    "z = df['DllCharacteristics'].tolist()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.scatter(x, y, z, c='r', marker='o')\n",
    "\n",
    "ax.set_xlabel('Point')\n",
    "ax.set_ylabel('TotalSuspiciousSections')\n",
    "ax.set_zlabel('DllCharacteristics')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
